_base_ = [
    "../_base_/models/ssd300.py",
    "../_base_/datasets/coco_detection.py",
    "../_base_/schedules/schedule_2x.py",
    "../_base_/default_runtime.py",
]
# dataset settings
dataset_type = "CocoDataset"
data_root = "data/coco/"
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
train_pipeline = [
    dict(type="LoadImageFromFile", to_float32=True),
    dict(type="LoadAnnotations", with_bbox=True),
    dict(
        type="PhotoMetricDistortion",
        brightness_delta=32,
        contrast_range=(0.5, 1.5),
        saturation_range=(0.5, 1.5),
        hue_delta=18,
    ),
    dict(
        type="Expand",
        mean=img_norm_cfg["mean"],
        to_rgb=img_norm_cfg["to_rgb"],
        ratio_range=(1, 4),
    ),
    dict(
        type="MinIoURandomCrop", min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), min_crop_size=0.3
    ),
    dict(type="Resize", img_scale=(300, 300), keep_ratio=False),
    dict(type="Normalize", **img_norm_cfg),
    dict(type="RandomFlip", flip_ratio=0.5),
    dict(type="DefaultFormatBundle"),
    dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels"]),
]
test_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(
        type="MultiScaleFlipAug",
        img_scale=(300, 300),
        flip=False,
        transforms=[
            dict(type="Resize", keep_ratio=False),
            dict(type="Normalize", **img_norm_cfg),
            dict(type="ImageToTensor", keys=["img"]),
            dict(type="Collect", keys=["img"]),
        ],
    ),
]
data = dict(
    samples_per_gpu=8,
    workers_per_gpu=3,
    train=dict(
        _delete_=True,
        type="RepeatDataset",
        times=5,
        dataset=dict(
            type=dataset_type,
            ann_file=data_root + "annotations/instances_train2017.json",
            img_prefix=data_root + "train2017/",
            pipeline=train_pipeline,
        ),
    ),
    val=dict(pipeline=test_pipeline),
    test=dict(pipeline=test_pipeline),
)
# optimizer
optimizer = dict(type="SGD", lr=2e-3, momentum=0.9, weight_decay=5e-4)
optimizer_config = dict(_delete_=True)
